Model2 Regressions Initial
Mike Wise
18 Mar 2017
Data pre-processed and analyzied from chat logs and web logs by Marcus Collins.
Analysis based on R-Scripts from Jacob LaRiviere.
R-Markdown and plots from Mike Wise.
Initialization of libraries
library(tidyverse,quietly=T,warn.conflicts=F)
library(lubridate,quietly=T,warn.conflicts=F)
library(scales,quietly=T,warn.conflicts=F)
library(zoo,quietly=T,warn.conflicts=F)
library(lmtest,quietly=T,warn.conflicts=F)
library(sandwich,quietly=T,warn.conflicts=F)
library(gridExtra,quietly=T,warn.conflicts = F)
library(knitr,quietly=T,warn.conflicts = F)Set random seeds, record start time and version.
set.seed(1234)
version <-0.1
versionstring <- sprintf("Version %.1f",version)
starttime <- Sys.time()
startfmttime <- sprintf(format(starttime, "%d %b %Y - %H:%M:%S"))
print(sprintf("%s created on %s",versionstring,startfmttime))## [1] "Version 0.1 created on 24 Mar 2017 - 16:55:10"Various constants
tztz <- "UTC" # Apparently all of our time zones are UTC...
s2date <- function(strdate){
return(as.POSIXct(strdate,tz=tztz))
}
firstday <- s2date("2015-01-01") # we will count days from the first day in 2015
smcdates <- c("2016-08-17/red/0-30%","2016-09-1/red/30-50%","2016-09-07/red/50-100%")
xbxdates <- c("2016-10-11/purple/0-10%","2016-10-18/purple/10-30%","2016-11-01/purple/30-50%","2016-12-15/purple/50-90%",
"2016-11-17/blue/content change")
totdates <- c(smcdates,xbxdates)
smcback <- "lightsteelblue1"
xbxback <- "darkseagreen2"
xabback <- "darkseagreen3"
totback <- "wheat"
fpath <- "../TorontoData"
sdate <- s2date("2016-06-01")
mdate <- s2date("2017-01-01")
edate <- s2date("2017-03-07")
verbose <- 2
justdoone <- F # for testingMisc utility functions
crackdate <- function(datestr){
sar <- unlist(strsplit(datestr,"/"))
sdate <- sar[[1]]
date <- s2date(sdate)
val <- 0
sval <- sar[[3]]
levpart <- sar[[3]]
pctpresent <- F
if (grepl("%",levpart)){
levpart <- gsub("%","",levpart)
val <- as.numeric(unlist(strsplit(levpart,"-"))[[2]])
sval <- gsub("-","_",levpart)
pctpresent <- T
}
return(list(date=date,sdate=sdate,val=val,sval=sval,pctpresent=pctpresent))
}
addStepDateToVek <- function(dates,idx,dtvek,vvek){
cd1 <- crackdate(dates[[idx]])
if (!cd1$pctpresent){
# if there is no % don't do anything
return(vvek)
}
dt1 <- cd1$date
if (idx<length(dates)){
cd2 <- crackdate(dates[[idx+1]])
dt2 <- cd2$date
} else {
dt2 <- max(dtvek)
}
#print("addstepdate")
#print(dt1)
#print(dt2)
val <- cd1$val
tochg <- dt1<=dtvek & dtvek<= dt2
vvek[ tochg ] <- val
#print(sprintf("changed %d values to %d",sum(tochg),val))
return(vvek)
}
getStepDates <- function(dates,dtvek){
vvek <- rep(0,length(dtvek))
for (i in 1:length(dates)){
vvek <- addStepDateToVek(dates,i,dtvek,vvek)
}
return(vvek)
}
getSmcStepDates <- function(dtvek){
return(getStepDates(smcdates,dtvek))
}
getXabStepDates <- function(dtvek){
return(getStepDates(xbxdates,dtvek))
}Plot functions
addVlinesAndText <- function(vlines,gp){
if (is.null(vlines)) return(gp) # do nothing in this case
# split the lines and convert to data.frame
sar <- strsplit(vlines,"/")
# the following reforms the date strings into a data.frame for geom_vline
ldf <- data.frame(t(matrix(unlist(sar),length(sar[[1]]),length(sar)))) #tricky
names(ldf) <- c("dt","clr","lab")
ldf$dt <- s2date(ldf$dt)
ldf$ndt <- as.numeric(ldf$dt)
# add a newline to the front so as to display the text
# this keeps the text from writing on top of the vline
ldf$lab <- paste0("\n",ldf$lab)
# now actually add the verticle lines and the text
gp <- gp + geom_vline(xintercept=ldf$ndt,color=ldf$clr) +
annotate(geom="text",x=ldf$dt,y=0,label=ldf$lab,color=ldf$clr,hjust=0,angle=90,na.rm=T)
return(gp)
}
addBackground <- function(backg,gp){
if (is.null(backg)) return(gp) # do nothing in this case
gp <- gp + theme(panel.background = element_rect(fill = backg))
return(gp)
}
overdate <- function(ovdate,defdate){
# date override
rv <- defdate
if (!is.null(ovdate)) {
rv <- ovdate
}
return(rv)
}
dailyplot <- function(ddf,x,y,mtit="",xlab="date",ylab=NULL,vlines=NULL,backg=NULL,series=NULL,ovsdate=NULL,ovedate=NULL){
# Single series plot with monthly breaks on the x-axis
# override dates if needed
dpsdate <- overdate(ovsdate,sdate)
dpedate <- overdate(ovedate,edate)
brkctrl <- "1 month"
dltdays <- difftime(dpedate,dpsdate,"days")
if (dltdays<30) brkctrl <- "1 day"
gp <- ggplot(ddf,aes_string(x=x,y=y)) +
geom_line(aes_string(color=series),na.rm=T) +
xlab(xlab) + ylab(ylab) + ggtitle(mtit) +
scale_x_datetime("Date",breaks = date_breaks(brkctrl),limits=c(dpsdate,dpedate))
gp <- addVlinesAndText(vlines,gp)
gp <- addBackground(backg,gp)
return(gp)
}
residplot <- function(ddf,x,y,mtit="",xlab="date",ylab=NULL,vlines=NULL,backg=NULL,series=NULL,ovsdate=NULL,ovedate=NULL){
# Single series plot with monthly breaks on the x-axis
# override dates if needed
dpsdate <- overdate(ovsdate,sdate)
dpedate <- overdate(ovedate,edate)
brkctrl <- "1 month"
dltdays <- difftime(dpedate,dpsdate,"days")
if (dltdays<30) brkctrl <- "1 day"
gp <- ggplot(ddf,aes_string(x=x,y=y)) +
geom_point(aes_string(color=series),na.rm=T) +
xlab(xlab) + ylab(ylab) + ggtitle(mtit) +
scale_x_datetime("Date",breaks = date_breaks(brkctrl),limits=c(dpsdate,dpedate)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1))
gp <- addVlinesAndText(vlines,gp)
gp <- addBackground(backg,gp)
hp <- ggplot(ddf) + geom_histogram(aes_string(x=y),bins=30)
hp <- addBackground(backg,hp)
ghp <- grid.arrange(gp, hp, ncol=2,widths = c(2,1))
return(ghp)
}Read in consolidated chat, call, and session volume data
stload <- Sys.time()
tfname <- sprintf("%s/%s",fpath,"colsolidatedTorontoData01.csv")
condf <- read.csv(tfname)
minsessfilt <- 5000
nbef <- nrow(condf)
condf <- condf %>% filter(minsessfilt<actsess)
naft <- nrow(condf)
print(sprintf("Filtered %d of %d hours because sessions less than %d",(nbef-naft),naft,minsessfilt))## [1] "Filtered 9 of 5815 hours because sessions less than 5000"condf <- condf %>% mutate( dt = as.POSIXct(dt,tz=tztz) ) %>%
mutate( log_winchib = log(winchib) ) %>%
mutate( log_wincall = log(wincall) ) %>%
mutate( log_xbxchib = log(xbxchib) ) %>%
mutate( log_xbxcall = log(xbxcall) ) %>%
mutate( rate_winchib = winchib/actsess ) %>%
mutate( rate_wincall = wincall/actsess ) %>%
mutate( rate_xbxchib = xbxchib/actsess ) %>%
mutate( rate_xbxcall = xbxcall/actsess )
dcondf <- condf %>% group_by(dnum) %>% summarise(dt=min(dt),
totchib=sum(totchib),winchib=sum(winchib),xbxchib=sum(xbxchib),
totcall=sum(totcall),wincall=sum(wincall),xbxcall=sum(xbxcall),
actsess=sum(actsess),actuser=sum(actuser)
)
elap <- as.numeric((Sys.time()-stload)[1],units="secs")
print(sprintf("Loading consolidated data took %.1f secs",elap))## [1] "Loading consolidated data took 0.1 secs"chats
if (verbose>=2){
pltdf <- dcondf %>% gather(series,chib,-dt) %>% filter(series %in% c("totchib","winchib","xbxchib"))
dailyplot(pltdf,"dt","chib",series="series",mtit="Chats In Block",ylab="Sum",vlines=totdates,backg=totback)
}
# calls
if (verbose>=2){
pltdf <- dcondf %>% gather(series,call,-dt) %>% filter(series %in% c("totcall","wincall","xbxcall"))
dailyplot(pltdf,"dt","call",series="series",mtit="Calls",ylab="Sum",vlines=totdates,backg=totback)
}
# sessions
if (verbose>=2){
pltdf <- dcondf %>% gather(series,active,-dt) %>% filter(series %in% c("actsess","actuser"))
dailyplot(pltdf,"dt","active",series="series",mtit="Sessions",ylab="Sum",vlines=totdates,backg=totback)
}
regression code
results1 <- list()
results2 <- list()
initResults <- function(){
results1 <<- list()
results2 <<- list()
}
addToResults1 <- function(newresults){
results1[[length(results1)+1]] <<- newresults
}
addToResults2 <- function(newresults){
results2[[length(results2)+1]] <<- newresults
}
listtodf <- function(lst){
nr <- length(lst) # rows
if (nr==0) return(NULL)
nvek <- names(lst[[1]])
nc <- length(nvek) # columns
df <- data.frame(idx=1:nr) # preallocate length
for (i in 1:nc){
iname <- nvek[i]
df[[iname]] <- sapply(lst,`[[`,iname)
}
return(df)
}
getResults1 <- function(){
return(listtodf(results1))
}
getResults2 <- function(){
return(listtodf(results2))
}
getregdf <- function(df,vname,discdate,befdays,aftdays,dates){
# filter on time
sregdate <- discdate - days(befdays)
eregdate <- discdate + days(aftdays)
df <- df %>% filter( sregdate <= dt & dt <= eregdate)
df <- df %>% mutate( hour=as.factor(hour(dt)) ) %>%
mutate( dow=as.factor(wday(dt))) %>%
mutate( idx=1:nrow(df) )
# add level variables
cutidx <- which(df$dt==discdate)
df <- df %>% mutate( lin1=if_else(idx<cutidx,idx-cutidx+1L,0L)) %>%
mutate( lin2=if_else(idx<cutidx,0L,idx-cutidx+1L)) %>%
mutate( lin0=1 ) %>%
mutate( postchange=if_else(idx<cutidx,0L,1L) )
return(df)
}
formlist = list(
formel1="%s ~ hour + dow + lin1 + lin2 + postchange",
formel2a="%s ~ hour * dow - 1",
formel2b="%s ~ lin1 + lin2 + postchange"
# formel2b="%s ~ lin0 + postchange"
)
doregression <- function(df,vname,formel,chgdate,befdays,aftdays){
df <- getregdf(df,vname,chgdate,befdays,aftdays,dates)
formstr <- sprintf(formlist[[formel]],vname)
form <- as.formula(formstr)
fit <- glm(form,data=df)
summary_fit <- summary(fit)
coeftest_fit <- coeftest(fit, vcov = vcovHC(fit, "HC1"))
if (verbose>0){
print(summary_fit)
print(coeftest_fit)
}
df$resid <- resid(fit)
df$predicted <- df[[vname]]-df$resid
# for debugging
ffit <<- fit
sfit <<- summary_fit
cfit <<- coeftest_fit
return(list(df=df,formstr=formstr,fit=fit,sfit=summary_fit,cfit=coeftest_fit))
}
regress1 <- function(df,vname,area,backg,dates,chgdate,mtit1,befdays=7,aftdays=7,model="model1"){
crk <- crackdate(chgdate)
idx <- length(results1)+1
print(sprintf("%d Regression for %s %s on %s",idx,vname,model,chgdate))
rv <- doregression(df,vname,"formel1",crk$date,befdays,aftdays)
if (verbose>0){
df1 <- rv$df %>% gather_("series",vname,c(vname,"predicted"))
mtit <- sprintf("%d - %s - %s",idx,mtit1,rv$formstr)
plt <- dailyplot(df1,"dt",vname,series="series",mtit,ylab="Sum",
vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
print(plt)
mtit <- sprintf("%d - Residuals - %s - %s",idx,mtit1,rv$formstr)
plt <- residplot(df1,"dt","resid",mtit,ylab="Sum",
vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
}
newresults <- list(var=vname,
title=mtit1,
chgDate=crk$sdate,
chgVal=crk$sval,
model=model,
formula=rv$formstr,
chgCoef.fit=rv$fit$coefficients["postchange"],
chgCoef.smry=rv$sfit$coefficients["postchange",1],
chgStd.smry=rv$sfit$coefficients["postchange",2],
chgTval.smry=rv$sfit$coefficients["postchange",3],
chgPval.smry=rv$sfit$coefficients["postchange",4]
)
addToResults1(newresults)
}
regress2 <- function(df,vname,area,backg,dates,chgdate,mtit1,befdays=7,aftdays=7,model="model2"){
crk <- crackdate(chgdate)
idx <- length(results1)+1
print(sprintf("%d Regression for %s %s on %s",idx,vname,model,chgdate))
# step 1
rv1 <- doregression(df,vname,"formel2b",crk$date,befdays,aftdays)
if (verbose>0){
df1 <- rv1$df %>% gather_("series",vname,c(vname,"predicted"))
mtit <- sprintf("%d - %s - %s",idx,mtit1,rv1$formstr)
plt <- dailyplot(df1,"dt",vname,series="series",mtit,ylab="Sum",
vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
print(plt)
mtit <- sprintf("%d - Step 1 Residuals - %s - %s",idx,mtit1,rv1$formstr)
plt <- residplot(df1,"dt","resid",mtit,ylab="Sum",
vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
}
newresults <- list(var=vname,
title=mtit1,
chgDate=crk$sdate,
chgVal=crk$sval,
model=model,
formula=rv1$formstr,
aic=rv1$fit$aic,
deviance=rv1$fit$dev
)
addToResults1(newresults)
# step 2
df2 <- rv1$df
df2$step1residuals <- rv1$fit$residuals
vname2 <- "step1residuals"
rv2 <- doregression(df2,vname2,"formel2b",crk$date,7,7)
if (verbose>0){
df1 <- rv2$df %>% gather_("series",vname2,c(vname2,"predicted"))
mtit <- sprintf("%d - %s - %s",idx,mtit1,rv2$formstr)
plt <- dailyplot(df1,"dt",vname2,series="series",mtit,ylab="Sum",
vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
print(plt)
mtit <- sprintf("%d - Step 2 Residuals - %s - %s",idx,mtit1,rv2$formstr)
plt <- residplot(df1,"dt","resid",mtit,ylab="Sum",
vlines=dates,backg=backg,ovsdate=min(df1$dt),ovedate=max(df1$dt))
}
newresults <- list(var=vname,
title=mtit1,
chgDate=crk$sdate,
chgVal=crk$sval,
model=model,
formula=rv2$formstr,
chgCoef.fit=rv2$fit$coefficients["postchange"],
chgCoef.smry=rv2$sfit$coefficients["postchange",1],
chgStd.smry=rv2$sfit$coefficients["postchange",2],
chgTval.smry=rv2$sfit$coefficients["postchange",3],
chgPval.smry=rv2$sfit$coefficients["postchange",4]
)
addToResults2(newresults)
}
regressoverdates <- function(model,df,vname,area,title,befdays=7,aftdays=7){
if (model=="model2" & grepl("^log",vname)) return(NULL) # model2 is pointless for logs
if (area=="SMC"){
dates <- smcdates
backg <- smcback
} else {
dates <- xbxdates
backg <- xbxback
}
atit <- sprintf("%s - %s",area,title)
for (chgdate in dates){
if (model=="model1"){
regress1(condf,vname,area,backg,dates,chgdate,atit,befdays=7,aftdays=7)
} else {
regress2(condf,vname,area,backg,dates,chgdate,atit,befdays=28,aftdays=28)
}
}
}initResults()
justdoone <- F # for testing
verbose <- 2
m <- "model2"
if (justdoone){
regressoverdates(m,condf,"winchib","SMC","Hourly Chats")
# regressoverdates(m,condf,"winchib","Xbox","Hourly Chats")
} else {
regressoverdates(m,condf,"winchib","SMC","Hourly Chats")
regressoverdates(m,condf,"log_winchib","SMC","log(Hourly Chats)")
regressoverdates(m,condf,"rate_winchib","SMC","Hourly Chat Rate per Session")
regressoverdates(m,condf,"wincall","SMC","Hourly Chats")
regressoverdates(m,condf,"log_wincall","SMC","log(Hourly Chats)")
regressoverdates(m,condf,"rate_wincall","SMC","Hourly Chat Rate per Session")
regressoverdates(m,condf,"xbxchib","Xbox","Hourly Chats")
regressoverdates(m,condf,"log_xbxchib","Xbox","log(Hourly Chats)")
regressoverdates(m,condf,"rate_xbxchib","Xbox","Hourly Chat Rate per Session")
regressoverdates(m,condf,"xbxcall","Xbox","Hourly Chats")
regressoverdates(m,condf,"log_xbxcall","Xbox","log(Hourly Chats)")
regressoverdates(m,condf,"rate_xbxcall","Xbox","Hourly Chat Rate per Session")
}## [1] "1 Regression for winchib model2 on 2016-08-17/red/0-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -329.56 -92.35 -10.22 88.00 344.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 337.53214 8.45516 39.920 < 2e-16 ***
## lin1 0.14941 0.02182 6.848 1.13e-11 ***
## lin2 -0.01278 0.02177 -0.587 0.5573
## postchange -21.19718 11.96630 -1.771 0.0767 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12037.11)
##
## Null deviance: 16914320 on 1344 degrees of freedom
## Residual deviance: 16141763 on 1341 degrees of freedom
## AIC: 16460
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 337.53214 9.10096 37.0875 < 2.2e-16 ***
## lin1 0.14941 0.02096 7.1284 1.015e-12 ***
## lin2 -0.01278 0.02198 -0.5814 0.56096
## postchange -21.19718 12.46115 -1.7011 0.08893 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -308.35 -101.72 -20.54 93.90 301.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -28.4902 17.9232 -1.590 0.113
## lin1 -0.1820 0.1856 -0.980 0.328
## lin2 0.2122 0.1840 1.154 0.250
## postchange 9.6937 25.4228 0.381 0.703
##
## (Dispersion parameter for gaussian family taken to be 13612.98)
##
## Null deviance: 4577565 on 336 degrees of freedom
## Residual deviance: 4533122 on 333 degrees of freedom
## AIC: 4170.2
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -28.49022 20.47929 -1.3912 0.1642
## lin1 -0.18199 0.21927 -0.8300 0.4066
## lin2 0.21222 0.18952 1.1198 0.2628
## postchange 9.69372 27.25017 0.3557 0.7220
## [1] "2 Regression for winchib model2 on 2016-09-1/red/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -311.79 -106.43 -7.10 95.99 364.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 319.95367 9.18342 34.840 < 2e-16 ***
## lin1 0.01540 0.02370 0.650 0.5159
## lin2 0.10116 0.02364 4.279 2.01e-05 ***
## postchange -29.49736 12.99698 -2.270 0.0234 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 14199.96)
##
## Null deviance: 19340147 on 1344 degrees of freedom
## Residual deviance: 19042152 on 1341 degrees of freedom
## AIC: 16682
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 319.953668 8.418529 38.0059 < 2.2e-16 ***
## lin1 0.015399 0.021616 0.7124 0.4762193
## lin2 0.101163 0.026290 3.8480 0.0001191 ***
## postchange -29.497355 12.580011 -2.3448 0.0190383 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -180.396 -104.618 -3.042 96.365 267.788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.0708 17.1706 1.402 0.1619
## lin1 0.3696 0.1778 2.078 0.0385 *
## lin2 0.1533 0.1762 0.870 0.3851
## postchange -19.8954 24.3552 -0.817 0.4146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12493.7)
##
## Null deviance: 4272312 on 336 degrees of freedom
## Residual deviance: 4160403 on 333 degrees of freedom
## AIC: 4141.3
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 24.07078 16.54180 1.4551 0.14563
## lin1 0.36955 0.17766 2.0801 0.03752 *
## lin2 0.15329 0.20499 0.7478 0.45459
## postchange -19.89543 25.94392 -0.7669 0.44316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "3 Regression for winchib model2 on 2016-09-07/red/50-100%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -310.56 -111.19 -6.44 99.84 368.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 314.340188 9.366936 33.558 < 2e-16 ***
## lin1 0.003276 0.024170 0.136 0.892
## lin2 0.109634 0.024116 4.546 5.96e-06 ***
## postchange -17.344672 13.256708 -1.308 0.191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 14773.16)
##
## Null deviance: 20260491 on 1344 degrees of freedom
## Residual deviance: 19810810 on 1341 degrees of freedom
## AIC: 16736
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 314.3401879 8.7945614 35.7426 < 2.2e-16 ***
## lin1 0.0032761 0.0233650 0.1402 0.8885
## lin2 0.1096336 0.0250691 4.3733 1.224e-05 ***
## postchange -17.3446720 12.7163480 -1.3640 0.1726
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -189.459 -111.550 -4.187 100.050 249.433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.8945 17.4735 -0.623 0.533
## lin1 -0.1153 0.1810 -0.637 0.524
## lin2 -0.2278 0.1794 -1.270 0.205
## postchange 30.8848 24.7849 1.246 0.214
##
## (Dispersion parameter for gaussian family taken to be 12938.43)
##
## Null deviance: 4334930 on 336 degrees of freedom
## Residual deviance: 4308498 on 333 degrees of freedom
## AIC: 4153
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -10.89450 18.25190 -0.5969 0.5506
## lin1 -0.11535 0.19308 -0.5974 0.5502
## lin2 -0.22781 0.18932 -1.2033 0.2289
## postchange 30.88482 26.08501 1.1840 0.2364
## [1] "4 Regression for rate_winchib model2 on 2016-08-17/red/0-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.630e-03 -5.747e-04 -1.389e-05 5.588e-04 2.294e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.680e-03 5.960e-05 44.964 < 2e-16 ***
## lin1 7.038e-07 1.538e-07 4.576 5.17e-06 ***
## lin2 1.115e-07 1.535e-07 0.726 0.468
## postchange -6.945e-05 8.436e-05 -0.823 0.410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.981914e-07)
##
## Null deviance: 0.00082905 on 1344 degrees of freedom
## Residual deviance: 0.00080217 on 1341 degrees of freedom
## AIC: -15450
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6801e-03 5.9541e-05 45.0125 < 2.2e-16 ***
## lin1 7.0385e-07 1.4167e-07 4.9681 6.762e-07 ***
## lin2 1.1146e-07 1.6138e-07 0.6907 0.4898
## postchange -6.9449e-05 8.4614e-05 -0.8208 0.4118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.500e-03 -5.791e-04 2.867e-05 5.534e-04 2.012e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.551e-04 1.194e-04 -1.299 0.195
## lin1 -6.290e-07 1.237e-06 -0.509 0.611
## lin2 6.010e-07 1.226e-06 0.490 0.624
## postchange 1.103e-04 1.694e-04 0.651 0.516
##
## (Dispersion parameter for gaussian family taken to be 6.045205e-07)
##
## Null deviance: 0.00020260 on 336 degrees of freedom
## Residual deviance: 0.00020131 on 333 degrees of freedom
## AIC: -3863.1
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5515e-04 1.2441e-04 -1.2470 0.2124
## lin1 -6.2900e-07 1.2376e-06 -0.5083 0.6113
## lin2 6.0103e-07 1.1572e-06 0.5194 0.6035
## postchange 1.1027e-04 1.6646e-04 0.6624 0.5077
## [1] "5 Regression for rate_winchib model2 on 2016-09-1/red/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.572e-03 -6.353e-04 -3.224e-05 6.126e-04 2.451e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.598e-03 6.300e-05 41.238 <2e-16 ***
## lin1 1.002e-08 1.626e-07 0.062 0.951
## lin2 -7.335e-08 1.622e-07 -0.452 0.651
## postchange 7.843e-05 8.916e-05 0.880 0.379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 6.682508e-07)
##
## Null deviance: 0.00089736 on 1344 degrees of freedom
## Residual deviance: 0.00089612 on 1341 degrees of freedom
## AIC: -15301
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5979e-03 5.9582e-05 43.6030 <2e-16 ***
## lin1 1.0018e-08 1.5323e-07 0.0654 0.9479
## lin2 -7.3354e-08 1.7438e-07 -0.4207 0.6740
## postchange 7.8426e-05 8.7571e-05 0.8956 0.3705
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.948e-03 -6.086e-04 -6.147e-05 5.884e-04 1.983e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.223e-06 1.231e-04 0.026 0.979
## lin1 7.640e-08 1.275e-06 0.060 0.952
## lin2 5.620e-07 1.263e-06 0.445 0.657
## postchange 4.117e-05 1.746e-04 0.236 0.814
##
## (Dispersion parameter for gaussian family taken to be 6.420869e-07)
##
## Null deviance: 0.00021471 on 336 degrees of freedom
## Residual deviance: 0.00021381 on 333 degrees of freedom
## AIC: -3842.8
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.2231e-06 1.1050e-04 0.0292 0.9767
## lin1 7.6404e-08 1.2099e-06 0.0631 0.9496
## lin2 5.6202e-07 1.3595e-06 0.4134 0.6793
## postchange 4.1165e-05 1.6773e-04 0.2454 0.8061
## [1] "6 Regression for rate_winchib model2 on 2016-09-07/red/50-100%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.513e-03 -6.662e-04 -6.801e-05 6.466e-04 2.771e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.761e-03 6.454e-05 42.781 < 2e-16 ***
## lin1 4.143e-07 1.665e-07 2.488 0.01297 *
## lin2 4.391e-07 1.662e-07 2.643 0.00832 **
## postchange -2.532e-04 9.134e-05 -2.772 0.00564 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7.013206e-07)
##
## Null deviance: 0.00095009 on 1344 degrees of freedom
## Residual deviance: 0.00094047 on 1341 degrees of freedom
## AIC: -15236
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.7610e-03 6.1570e-05 44.8438 < 2.2e-16 ***
## lin1 4.1432e-07 1.5650e-07 2.6475 0.008109 **
## lin2 4.3908e-07 1.8046e-07 2.4331 0.014969 *
## postchange -2.5324e-04 9.0204e-05 -2.8074 0.004995 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.047e-03 -6.530e-04 -3.185e-05 6.318e-04 2.361e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.441e-04 1.259e-04 1.145 0.253
## lin1 1.482e-06 1.304e-06 1.137 0.256
## lin2 -1.379e-06 1.292e-06 -1.067 0.287
## postchange 5.149e-05 1.785e-04 0.288 0.773
##
## (Dispersion parameter for gaussian family taken to be 6.713686e-07)
##
## Null deviance: 0.00022548 on 336 degrees of freedom
## Residual deviance: 0.00022357 on 333 degrees of freedom
## AIC: -3827.8
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.4410e-04 1.4031e-04 1.0270 0.3044
## lin1 1.4825e-06 1.3660e-06 1.0853 0.2778
## lin2 -1.3790e-06 1.2547e-06 -1.0991 0.2717
## postchange 5.1489e-05 1.8743e-04 0.2747 0.7835
## [1] "7 Regression for wincall model2 on 2016-08-17/red/0-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -461.10 -265.39 -23.99 223.27 1089.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 354.14244 21.59879 16.396 < 2e-16 ***
## lin1 -0.23155 0.05573 -4.155 3.46e-05 ***
## lin2 -0.05600 0.05561 -1.007 0.314
## postchange 2.36151 30.56803 0.077 0.938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 78548.36)
##
## Null deviance: 109752259 on 1344 degrees of freedom
## Residual deviance: 105333345 on 1341 degrees of freedom
## AIC: 18983
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 354.142441 20.712629 17.0979 < 2.2e-16 ***
## lin1 -0.231550 0.059359 -3.9008 9.587e-05 ***
## lin2 -0.055997 0.048820 -1.1470 0.2514
## postchange 2.361512 28.422672 0.0831 0.9338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -359.95 -265.17 -24.33 206.62 477.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.9870 39.8966 1.077 0.282
## lin1 0.5103 0.4132 1.235 0.218
## lin2 0.3148 0.4095 0.769 0.443
## postchange -63.1475 56.5905 -1.116 0.265
##
## (Dispersion parameter for gaussian family taken to be 67451.82)
##
## Null deviance: 22607490 on 336 degrees of freedom
## Residual deviance: 22461457 on 333 degrees of freedom
## AIC: 4709.5
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 42.98696 42.43070 1.0131 0.3110
## lin1 0.51033 0.44193 1.1548 0.2482
## lin2 0.31482 0.42743 0.7365 0.4614
## postchange -63.14751 59.08883 -1.0687 0.2852
## [1] "8 Regression for wincall model2 on 2016-09-1/red/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -422.83 -252.59 -23.03 214.04 646.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 361.904214 20.144737 17.965 < 2e-16 ***
## lin1 0.007818 0.051980 0.150 0.880468
## lin2 0.310346 0.051864 5.984 2.79e-09 ***
## postchange -95.279943 28.510165 -3.342 0.000855 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 68328.44)
##
## Null deviance: 94124403 on 1344 degrees of freedom
## Residual deviance: 91628440 on 1341 degrees of freedom
## AIC: 18796
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 361.9042135 19.4633348 18.5942 < 2.2e-16 ***
## lin1 0.0078181 0.0500746 0.1561 0.8759318
## lin2 0.3103464 0.0555125 5.5906 2.263e-08 ***
## postchange -95.2799432 26.8441290 -3.5494 0.0003861 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -347.50 -224.38 -11.09 185.74 466.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.5168 36.2639 1.090 0.2766
## lin1 0.6297 0.3756 1.677 0.0945 .
## lin2 0.1458 0.3722 0.392 0.6956
## postchange -30.3619 51.4377 -0.590 0.5554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 55727.55)
##
## Null deviance: 18823387 on 336 degrees of freedom
## Residual deviance: 18557274 on 333 degrees of freedom
## AIC: 4645.2
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 39.51682 39.75431 0.9940 0.3202
## lin1 0.62967 0.40423 1.5577 0.1193
## lin2 0.14578 0.39691 0.3673 0.7134
## postchange -30.36193 53.92056 -0.5631 0.5734
## [1] "9 Regression for wincall model2 on 2016-09-07/red/50-100%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -441.44 -260.31 -25.27 221.15 674.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 317.45007 20.70386 15.333 < 2e-16 ***
## lin1 -0.09387 0.05342 -1.757 0.0791 .
## lin2 0.24597 0.05330 4.615 4.32e-06 ***
## postchange -2.61066 29.30147 -0.089 0.9290
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 72174)
##
## Null deviance: 99345425 on 1344 degrees of freedom
## Residual deviance: 96785338 on 1341 degrees of freedom
## AIC: 18869
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 317.450068 18.253535 17.3912 < 2.2e-16 ***
## lin1 -0.093874 0.048988 -1.9163 0.05533 .
## lin2 0.245974 0.055367 4.4426 8.888e-06 ***
## postchange -2.610663 26.590701 -0.0982 0.92179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -331.76 -223.54 -24.72 175.40 505.89
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.9263 35.2945 -0.536 0.592
## lin1 -0.1019 0.3655 -0.279 0.781
## lin2 0.1673 0.3623 0.462 0.644
## postchange -2.3886 50.0627 -0.048 0.962
##
## (Dispersion parameter for gaussian family taken to be 52788)
##
## Null deviance: 17594703 on 336 degrees of freedom
## Residual deviance: 17578404 on 333 degrees of freedom
## AIC: 4626.9
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -18.92633 38.12619 -0.4964 0.6196
## lin1 -0.10188 0.40485 -0.2516 0.8013
## lin2 0.16735 0.39927 0.4191 0.6751
## postchange -2.38856 53.51419 -0.0446 0.9644
## [1] "10 Regression for rate_wincall model2 on 2016-08-17/red/0-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.003736 -0.001733 -0.000002 0.001468 0.010237
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.506e-03 1.466e-04 17.100 < 2e-16 ***
## lin1 -2.593e-06 3.782e-07 -6.855 1.08e-11 ***
## lin2 -1.245e-07 3.774e-07 -0.330 0.741
## postchange 1.314e-04 2.074e-04 0.633 0.527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.617212e-06)
##
## Null deviance: 0.0052258 on 1344 degrees of freedom
## Residual deviance: 0.0048507 on 1341 degrees of freedom
## AIC: -13030
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5063e-03 1.3290e-04 18.8583 < 2.2e-16 ***
## lin1 -2.5926e-06 4.0147e-07 -6.4578 1.062e-10 ***
## lin2 -1.2451e-07 3.1920e-07 -0.3901 0.6965
## postchange 1.3140e-04 1.8077e-04 0.7269 0.4673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0025573 -0.0015822 0.0000865 0.0014456 0.0031873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.280e-04 2.486e-04 1.721 0.0861 .
## lin1 4.811e-06 2.575e-06 1.869 0.0626 .
## lin2 1.200e-06 2.552e-06 0.470 0.6384
## postchange -4.794e-04 3.526e-04 -1.359 0.1749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.619271e-06)
##
## Null deviance: 0.00088199 on 336 degrees of freedom
## Residual deviance: 0.00087222 on 333 degrees of freedom
## AIC: -3369
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.2795e-04 2.5159e-04 1.7010 0.08895 .
## lin1 4.8109e-06 2.5070e-06 1.9190 0.05498 .
## lin2 1.2005e-06 2.4847e-06 0.4831 0.62900
## postchange -4.7942e-04 3.5028e-04 -1.3687 0.17111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "11 Regression for rate_wincall model2 on 2016-09-1/red/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0026911 -0.0016161 0.0000459 0.0013877 0.0103087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.644e-03 1.275e-04 20.745 < 2e-16 ***
## lin1 -8.086e-08 3.289e-07 -0.246 0.805823
## lin2 1.194e-06 3.281e-07 3.638 0.000285 ***
## postchange -3.124e-04 1.804e-04 -1.732 0.083547 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.73536e-06)
##
## Null deviance: 0.0037058 on 1344 degrees of freedom
## Residual deviance: 0.0036681 on 1341 degrees of freedom
## AIC: -13405
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6441e-03 1.2287e-04 21.5193 < 2.2e-16 ***
## lin1 -8.0862e-08 3.1581e-07 -0.2560 0.7979161
## lin2 1.1938e-06 3.4954e-07 3.4153 0.0006371 ***
## postchange -3.1239e-04 1.7827e-04 -1.7523 0.0797244 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0025323 -0.0015946 -0.0000797 0.0013247 0.0100585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.164e-04 2.594e-04 0.449 0.654
## lin1 1.910e-06 2.686e-06 0.711 0.477
## lin2 1.875e-06 2.662e-06 0.704 0.482
## postchange -1.361e-04 3.679e-04 -0.370 0.712
##
## (Dispersion parameter for gaussian family taken to be 2.850998e-06)
##
## Null deviance: 0.00095505 on 336 degrees of freedom
## Residual deviance: 0.00094938 on 333 degrees of freedom
## AIC: -3340.4
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.1638e-04 2.4055e-04 0.4838 0.6285
## lin1 1.9103e-06 2.4354e-06 0.7844 0.4328
## lin2 1.8751e-06 2.9171e-06 0.6428 0.5204
## postchange -1.3614e-04 3.3792e-04 -0.4029 0.6871
## [1] "12 Regression for rate_wincall model2 on 2016-09-07/red/50-100%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0028717 -0.0016597 0.0000482 0.0014252 0.0102239
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.588e-03 1.294e-04 20.001 < 2e-16 ***
## lin1 -1.891e-07 3.339e-07 -0.566 0.571254
## lin2 1.252e-06 3.332e-07 3.759 0.000178 ***
## postchange -1.824e-04 1.832e-04 -0.996 0.319488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 2.820075e-06)
##
## Null deviance: 0.0038329 on 1344 degrees of freedom
## Residual deviance: 0.0037817 on 1341 degrees of freedom
## AIC: -13364
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5884e-03 1.3847e-04 18.6927 < 2.2e-16 ***
## lin1 -1.8912e-07 3.4967e-07 -0.5409 0.5885991
## lin2 1.2524e-06 3.3383e-07 3.7515 0.0001758 ***
## postchange -1.8240e-04 1.8346e-04 -0.9942 0.3201061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0026141 -0.0015578 0.0000846 0.0012651 0.0099778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.461e-04 2.527e-04 0.974 0.331
## lin1 3.182e-06 2.617e-06 1.216 0.225
## lin2 1.308e-06 2.594e-06 0.504 0.614
## postchange -3.447e-04 3.585e-04 -0.962 0.337
##
## (Dispersion parameter for gaussian family taken to be 2.706322e-06)
##
## Null deviance: 0.00090598 on 336 degrees of freedom
## Residual deviance: 0.00090121 on 333 degrees of freedom
## AIC: -3358
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.4610e-04 3.6261e-04 0.6787 0.4973
## lin1 3.1820e-06 3.4216e-06 0.9300 0.3524
## lin2 1.3083e-06 2.3620e-06 0.5539 0.5797
## postchange -3.4468e-04 4.2724e-04 -0.8068 0.4198
## [1] "13 Regression for xbxchib model2 on 2016-10-11/purple/0-10%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -261.73 -114.81 0.72 103.88 340.75
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 237.84995 10.08577 23.583 < 2e-16 ***
## lin1 -0.10854 0.02642 -4.109 4.22e-05 ***
## lin2 0.02778 0.02589 1.073 0.283
## postchange -33.70708 14.23143 -2.368 0.018 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 16873.28)
##
## Null deviance: 23938381 on 1332 degrees of freedom
## Residual deviance: 22424584 on 1329 degrees of freedom
## AIC: 16764
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 237.849954 9.775393 24.3315 < 2.2e-16 ***
## lin1 -0.108543 0.028683 -3.7842 0.0001542 ***
## lin2 0.027780 0.023663 1.1740 0.2404107
## postchange -33.707080 13.052416 -2.5824 0.0098104 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -201.345 -95.963 0.902 92.236 295.222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.79001 17.99739 -0.044 0.9650
## lin1 0.08383 0.19823 0.423 0.6727
## lin2 0.39759 0.17920 2.219 0.0272 *
## postchange -22.08934 25.14643 -0.878 0.3804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12916.14)
##
## Null deviance: 4265111 on 326 degrees of freedom
## Residual deviance: 4171912 on 323 degrees of freedom
## AIC: 4029.4
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.790007 17.808799 -0.0444 0.96462
## lin1 0.083829 0.208501 0.4021 0.68764
## lin2 0.397592 0.170897 2.3265 0.01999 *
## postchange -22.089343 23.789510 -0.9285 0.35313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "14 Regression for xbxchib model2 on 2016-10-18/purple/10-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -259.49 -109.42 0.85 98.48 339.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 210.59009 9.59691 21.944 < 2e-16 ***
## lin1 -0.12342 0.02514 -4.910 1.03e-06 ***
## lin2 0.03684 0.02596 1.419 0.156
## postchange -10.09558 13.66101 -0.739 0.460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 15277.22)
##
## Null deviance: 20847384 on 1309 degrees of freedom
## Residual deviance: 19952048 on 1306 degrees of freedom
## AIC: 16344
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 210.590094 9.387721 22.4325 < 2.2e-16 ***
## lin1 -0.123416 0.028106 -4.3911 1.128e-05 ***
## lin2 0.036835 0.024546 1.5007 0.1334
## postchange -10.095580 12.767907 -0.7907 0.4291
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -198.650 -96.730 1.435 90.553 242.862
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.0204 16.9330 2.304 0.0218 *
## lin1 0.5208 0.1754 2.970 0.0032 **
## lin2 0.1763 0.1769 0.996 0.3198
## postchange -44.0174 24.0911 -1.827 0.0686 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12150.36)
##
## Null deviance: 4158081 on 334 degrees of freedom
## Residual deviance: 4021769 on 331 degrees of freedom
## AIC: 4107.4
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 39.02043 17.59629 2.2175 0.02659 *
## lin1 0.52080 0.17177 3.0320 0.00243 **
## lin2 0.17630 0.18367 0.9599 0.33712
## postchange -44.01743 24.67464 -1.7839 0.07444 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "15 Regression for xbxchib model2 on 2016-11-01/purple/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -221.21 -106.73 1.48 97.22 313.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 192.76212 9.13096 21.111 < 2e-16 ***
## lin1 -0.06898 0.02399 -2.876 0.0041 **
## lin2 0.10212 0.02455 4.160 3.38e-05 ***
## postchange 7.86980 12.97769 0.606 0.5443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 13788.08)
##
## Null deviance: 18470543 on 1309 degrees of freedom
## Residual deviance: 18007237 on 1306 degrees of freedom
## AIC: 16210
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 192.762119 8.006706 24.0751 < 2.2e-16 ***
## lin1 -0.068984 0.022178 -3.1105 0.001868 **
## lin2 0.102121 0.027085 3.7704 0.000163 ***
## postchange 7.869798 12.664837 0.6214 0.534343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -258.721 -104.999 0.911 89.538 262.169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.6324 17.3911 -0.554 0.5800
## lin1 -0.0803 0.1801 -0.446 0.6560
## lin2 0.4241 0.1785 2.376 0.0181 *
## postchange -6.2571 24.6680 -0.254 0.7999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12816.63)
##
## Null deviance: 4387701 on 336 degrees of freedom
## Residual deviance: 4267937 on 333 degrees of freedom
## AIC: 4149.9
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.632435 14.765107 -0.6524 0.51416
## lin1 -0.080304 0.162534 -0.4941 0.62125
## lin2 0.424056 0.195317 2.1711 0.02992 *
## postchange -6.257090 22.806075 -0.2744 0.78381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "16 Regression for xbxchib model2 on 2016-12-15/purple/50-90%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -260.51 -131.51 -7.33 98.77 581.93
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 203.53260 11.65075 17.469 < 2e-16 ***
## lin1 -0.07488 0.03006 -2.491 0.0129 *
## lin2 0.03752 0.03000 1.251 0.2113
## postchange 79.65488 16.48891 4.831 1.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 22855.25)
##
## Null deviance: 32343744 on 1344 degrees of freedom
## Residual deviance: 30648889 on 1341 degrees of freedom
## AIC: 17323
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 203.532597 8.399446 24.2317 < 2.2e-16 ***
## lin1 -0.074884 0.022612 -3.3117 0.0009272 ***
## lin2 0.037517 0.028041 1.3379 0.1809185
## postchange 79.654877 14.413105 5.5266 3.266e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -202.436 -109.211 6.105 94.351 260.782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.7977 17.2869 1.550 0.1220
## lin1 0.4279 0.1790 2.390 0.0174 *
## lin2 0.2258 0.1774 1.273 0.2040
## postchange -98.4643 24.5202 -4.016 7.33e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 12663.53)
##
## Null deviance: 4469535 on 336 degrees of freedom
## Residual deviance: 4216957 on 333 degrees of freedom
## AIC: 4145.8
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 26.79771 15.90717 1.6846 0.092060 .
## lin1 0.42786 0.15705 2.7244 0.006443 **
## lin2 0.22583 0.17616 1.2819 0.199862
## postchange -98.46426 23.59437 -4.1732 3.003e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "17 Regression for xbxchib model2 on 2016-11-17/blue/content change"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -212.17 -110.35 0.04 95.14 339.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 221.86927 9.25248 23.979 < 2e-16 ***
## lin1 0.02971 0.02480 1.198 0.23113
## lin2 -0.07336 0.02338 -3.138 0.00174 **
## postchange 31.64215 12.97246 2.439 0.01485 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 13879.3)
##
## Null deviance: 18511758 on 1319 degrees of freedom
## Residual deviance: 18265158 on 1316 degrees of freedom
## AIC: 16342
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 221.869268 9.388595 23.6318 < 2.2e-16 ***
## lin1 0.029708 0.024019 1.2368 0.216149
## lin2 -0.073356 0.022567 -3.2506 0.001152 **
## postchange 31.642154 13.274200 2.3837 0.017138 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -207.13 -115.09 6.48 104.16 277.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.8527 19.6549 0.247 0.805
## lin1 0.1087 0.2360 0.460 0.646
## lin2 0.3091 0.1876 1.648 0.100
## postchange -43.4400 26.9109 -1.614 0.107
##
## (Dispersion parameter for gaussian family taken to be 14149.28)
##
## Null deviance: 4434521 on 313 degrees of freedom
## Residual deviance: 4386278 on 310 degrees of freedom
## AIC: 3898.1
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.85270 18.79890 0.2581 0.79630
## lin1 0.10867 0.21665 0.5016 0.61595
## lin2 0.30912 0.17483 1.7681 0.07704 .
## postchange -43.43999 25.39479 -1.7106 0.08716 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "18 Regression for rate_xbxchib model2 on 2016-10-11/purple/0-10%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.002199 -0.001322 -0.000414 0.000980 0.050981
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.168e-03 1.815e-04 11.946 <2e-16 ***
## lin1 -5.476e-07 4.754e-07 -1.152 0.250
## lin2 4.033e-07 4.659e-07 0.866 0.387
## postchange -1.749e-04 2.561e-04 -0.683 0.495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.46381e-06)
##
## Null deviance: 0.0072889 on 1332 degrees of freedom
## Residual deviance: 0.0072614 on 1329 degrees of freedom
## AIC: -12364
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.1680e-03 1.0759e-04 20.1504 < 2e-16 ***
## lin1 -5.4758e-07 3.0442e-07 -1.7987 0.07206 .
## lin2 4.0333e-07 3.4675e-07 1.1632 0.24477
## postchange -1.7486e-04 2.2346e-04 -0.7825 0.43391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0020813 -0.0010879 -0.0002986 0.0010626 0.0036961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.142e-04 2.012e-04 1.561 0.11940
## lin1 4.396e-06 2.217e-06 1.983 0.04819 *
## lin2 5.723e-06 2.004e-06 2.856 0.00456 **
## postchange -9.049e-04 2.812e-04 -3.218 0.00142 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.614804e-06)
##
## Null deviance: 0.00054155 on 326 degrees of freedom
## Residual deviance: 0.00052158 on 323 degrees of freedom
## AIC: -3427
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.1422e-04 2.1398e-04 1.4684 0.1419854
## lin1 4.3958e-06 2.2546e-06 1.9497 0.0512114 .
## lin2 5.7232e-06 1.9198e-06 2.9811 0.0028723 **
## postchange -9.0488e-04 2.6856e-04 -3.3694 0.0007533 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "19 Regression for rate_xbxchib model2 on 2016-10-18/purple/10-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.002104 -0.001277 -0.000409 0.000932 0.050812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.993e-03 1.795e-04 11.099 <2e-16 ***
## lin1 -5.292e-07 4.703e-07 -1.125 0.261
## lin2 -5.384e-07 4.856e-07 -1.109 0.268
## postchange 3.243e-04 2.556e-04 1.269 0.205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.347008e-06)
##
## Null deviance: 0.0069967 on 1309 degrees of freedom
## Residual deviance: 0.0069832 on 1306 degrees of freedom
## AIC: -12178
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9927e-03 1.0387e-04 19.1852 < 2e-16 ***
## lin1 -5.2921e-07 2.8911e-07 -1.8305 0.06718 .
## lin2 -5.3836e-07 7.4513e-07 -0.7225 0.46998
## postchange 3.2427e-04 3.5859e-04 0.9043 0.36584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.002779 -0.001341 -0.000503 0.000689 0.050402
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.145e-04 5.992e-04 0.692 0.490
## lin1 6.435e-06 6.205e-06 1.037 0.301
## lin2 7.625e-06 6.261e-06 1.218 0.224
## postchange -7.128e-04 8.525e-04 -0.836 0.404
##
## (Dispersion parameter for gaussian family taken to be 1.521393e-05)
##
## Null deviance: 0.0050928 on 334 degrees of freedom
## Residual deviance: 0.0050358 on 331 degrees of freedom
## AIC: -2759.6
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.1446e-04 2.1081e-04 1.9660 0.0492937 *
## lin1 6.4346e-06 1.9414e-06 3.3144 0.0009183 ***
## lin2 7.6250e-06 2.8583e-06 2.6677 0.0076371 **
## postchange -7.1284e-04 4.0527e-04 -1.7589 0.0785942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "20 Regression for rate_xbxchib model2 on 2016-11-01/purple/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.002321 -0.001254 -0.000377 0.000912 0.050961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.117e-03 1.807e-04 11.717 < 2e-16 ***
## lin1 -4.501e-09 4.747e-07 -0.009 0.99244
## lin2 1.417e-06 4.857e-07 2.918 0.00358 **
## postchange -3.060e-04 2.568e-04 -1.192 0.23363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.399112e-06)
##
## Null deviance: 0.0071050 on 1309 degrees of freedom
## Residual deviance: 0.0070512 on 1306 degrees of freedom
## AIC: -12166
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.1172e-03 2.0732e-04 10.2119 < 2.2e-16 ***
## lin1 -4.5005e-09 3.5523e-07 -0.0127 0.9899
## lin2 1.4173e-06 3.2251e-07 4.3947 1.109e-05 ***
## postchange -3.0601e-04 2.3720e-04 -1.2901 0.1970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0027073 -0.0010789 -0.0002822 0.0009302 0.0041919
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.203e-04 2.048e-04 -1.075 0.28293
## lin1 1.385e-06 2.121e-06 0.653 0.51430
## lin2 6.943e-06 2.102e-06 3.303 0.00106 **
## postchange -8.542e-05 2.905e-04 -0.294 0.76890
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.777211e-06)
##
## Null deviance: 0.00064438 on 336 degrees of freedom
## Residual deviance: 0.00059181 on 333 degrees of freedom
## AIC: -3499.7
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2025e-04 1.7709e-04 -1.2437 0.213594
## lin1 1.3846e-06 1.7631e-06 0.7853 0.432269
## lin2 6.9430e-06 2.3199e-06 2.9928 0.002764 **
## postchange -8.5417e-05 2.5613e-04 -0.3335 0.738765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "21 Regression for rate_xbxchib model2 on 2016-12-15/purple/50-90%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0027293 -0.0014475 -0.0002937 0.0011457 0.0073375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.786e-03 1.391e-04 12.845 < 2e-16 ***
## lin1 -1.238e-06 3.588e-07 -3.450 0.000578 ***
## lin2 5.175e-08 3.580e-07 0.145 0.885091
## postchange 1.325e-03 1.968e-04 6.733 2.46e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.256275e-06)
##
## Null deviance: 0.0046946 on 1344 degrees of freedom
## Residual deviance: 0.0043667 on 1341 degrees of freedom
## AIC: -13171
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7864e-03 9.5242e-05 18.7560 < 2.2e-16 ***
## lin1 -1.2381e-06 2.7186e-07 -4.5540 5.264e-06 ***
## lin2 5.1753e-08 3.5101e-07 0.1474 0.8828
## postchange 1.3251e-03 1.7358e-04 7.6338 2.280e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0019586 -0.0011186 -0.0001618 0.0009957 0.0041645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.044e-04 2.020e-04 0.517 0.605737
## lin1 1.953e-06 2.092e-06 0.934 0.351234
## lin2 1.166e-06 2.074e-06 0.562 0.574398
## postchange -9.994e-04 2.865e-04 -3.488 0.000553 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.729419e-06)
##
## Null deviance: 0.00062374 on 336 degrees of freedom
## Residual deviance: 0.00057590 on 333 degrees of freedom
## AIC: -3508.9
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0437e-04 1.5504e-04 0.6732 0.5008196
## lin1 1.9530e-06 1.5822e-06 1.2344 0.2170693
## lin2 1.1656e-06 1.9575e-06 0.5955 0.5515181
## postchange -9.9936e-04 2.5936e-04 -3.8531 0.0001166 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "22 Regression for rate_xbxchib model2 on 2016-11-17/blue/content change"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.002198 -0.001255 -0.000356 0.000935 0.050606
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.777e-03 1.820e-04 9.762 < 2e-16 ***
## lin1 -1.156e-06 4.878e-07 -2.370 0.01791 *
## lin2 -1.223e-06 4.598e-07 -2.660 0.00791 **
## postchange 8.382e-04 2.552e-04 3.285 0.00105 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.37086e-06)
##
## Null deviance: 0.0071371 on 1319 degrees of freedom
## Residual deviance: 0.0070681 on 1316 degrees of freedom
## AIC: -12266
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7767e-03 1.9484e-04 9.1192 < 2.2e-16 ***
## lin1 -1.1564e-06 8.7568e-07 -1.3205 0.1866550
## lin2 -1.2231e-06 2.7122e-07 -4.5099 6.487e-06 ***
## postchange 8.3824e-04 2.2743e-04 3.6857 0.0002281 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.0019125 -0.0011896 -0.0002557 0.0010426 0.0056722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.249e-04 2.302e-04 0.542 0.588
## lin1 9.880e-08 2.764e-06 0.036 0.972
## lin2 7.431e-07 2.197e-06 0.338 0.735
## postchange -4.212e-04 3.152e-04 -1.336 0.182
##
## (Dispersion parameter for gaussian family taken to be 1.941461e-06)
##
## Null deviance: 0.00061169 on 313 degrees of freedom
## Residual deviance: 0.00060185 on 310 degrees of freedom
## AIC: -3232.7
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.2486e-04 1.8480e-04 0.6756 0.4993
## lin1 9.8799e-08 2.1492e-06 0.0460 0.9633
## lin2 7.4310e-07 2.0228e-06 0.3674 0.7133
## postchange -4.2120e-04 2.8979e-04 -1.4534 0.1461
## [1] "23 Regression for xbxcall model2 on 2016-10-11/purple/0-10%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1042.3 -760.0 31.5 617.0 3617.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.008e+03 5.691e+01 17.715 <2e-16 ***
## lin1 -9.568e-02 1.491e-01 -0.642 0.521
## lin2 -5.912e-03 1.461e-01 -0.040 0.968
## postchange -4.596e+01 8.031e+01 -0.572 0.567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 537293)
##
## Null deviance: 716394165 on 1332 degrees of freedom
## Residual deviance: 714062370 on 1329 degrees of freedom
## AIC: 21377
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0082e+03 5.5785e+01 18.0728 <2e-16 ***
## lin1 -9.5679e-02 1.5058e-01 -0.6354 0.5252
## lin2 -5.9123e-03 1.3833e-01 -0.0427 0.9659
## postchange -4.5957e+01 7.7550e+01 -0.5926 0.5534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1081.62 -721.32 34.45 617.83 1763.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.625 112.895 0.803 0.423
## lin1 1.300 1.243 1.045 0.297
## lin2 1.638 1.124 1.457 0.146
## postchange -190.531 157.740 -1.208 0.228
##
## (Dispersion parameter for gaussian family taken to be 508234)
##
## Null deviance: 166003861 on 326 degrees of freedom
## Residual deviance: 164159596 on 323 degrees of freedom
## AIC: 5230.3
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 90.6250 114.7811 0.7895 0.4298
## lin1 1.3001 1.2515 1.0388 0.2989
## lin2 1.6376 1.1029 1.4848 0.1376
## postchange -190.5308 152.7118 -1.2476 0.2122
## [1] "24 Regression for xbxcall model2 on 2016-10-18/purple/10-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1048.1 -741.4 21.1 603.7 3584.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 960.28947 55.82846 17.201 <2e-16 ***
## lin1 -0.20996 0.14623 -1.436 0.151
## lin2 -0.03477 0.15100 -0.230 0.818
## postchange -16.20591 79.47070 -0.204 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 517002.9)
##
## Null deviance: 679372762 on 1309 degrees of freedom
## Residual deviance: 675205830 on 1306 degrees of freedom
## AIC: 20958
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 960.289470 58.507021 16.4132 <2e-16 ***
## lin1 -0.209965 0.168604 -1.2453 0.2130
## lin2 -0.034774 0.142015 -0.2449 0.8066
## postchange -16.205912 79.791012 -0.2031 0.8391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1062.55 -727.14 27.27 619.67 1771.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 154.363 109.494 1.410 0.160
## lin1 1.646 1.134 1.451 0.148
## lin2 1.293 1.144 1.130 0.259
## postchange -211.034 155.781 -1.355 0.176
##
## (Dispersion parameter for gaussian family taken to be 508045.8)
##
## Null deviance: 169984586 on 334 degrees of freedom
## Residual deviance: 168163147 on 331 degrees of freedom
## AIC: 5358
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 154.3634 114.2789 1.3508 0.1768
## lin1 1.6458 1.1081 1.4852 0.1375
## lin2 1.2928 1.1539 1.1204 0.2625
## postchange -211.0344 158.5626 -1.3309 0.1832
## [1] "25 Regression for xbxcall model2 on 2016-11-01/purple/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -994.1 -733.8 14.6 594.5 1801.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 889.6170 53.6200 16.591 <2e-16 ***
## lin1 -0.2136 0.1409 -1.516 0.1297
## lin2 0.2411 0.1441 1.673 0.0946 .
## postchange -6.9056 76.2092 -0.091 0.9278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 475471.1)
##
## Null deviance: 623389131 on 1309 degrees of freedom
## Residual deviance: 620965201 on 1306 degrees of freedom
## AIC: 20848
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 889.61699 49.98253 17.7986 <2e-16 ***
## lin1 -0.21359 0.13559 -1.5752 0.1152
## lin2 0.24112 0.15436 1.5621 0.1183
## postchange -6.90557 74.47389 -0.0927 0.9261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1079.94 -697.82 61.97 553.81 1384.07
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -95.1894 101.2375 -0.940 0.348
## lin1 -0.5197 1.0484 -0.496 0.620
## lin2 1.4090 1.0391 1.356 0.176
## postchange 74.6513 143.5982 0.520 0.604
##
## (Dispersion parameter for gaussian family taken to be 434314.2)
##
## Null deviance: 147453366 on 336 degrees of freedom
## Residual deviance: 144626625 on 333 degrees of freedom
## AIC: 5337.1
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -95.18940 90.61505 -1.0505 0.2935
## lin1 -0.51967 0.98406 -0.5281 0.5974
## lin2 1.40898 1.05874 1.3308 0.1833
## postchange 74.65127 136.89999 0.5453 0.5855
## [1] "26 Regression for xbxcall model2 on 2016-12-15/purple/50-90%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1312.5 -852.7 -6.4 632.4 4494.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.021e+03 7.098e+01 14.381 < 2e-16 ***
## lin1 1.219e-01 1.831e-01 0.665 0.50586
## lin2 4.927e-02 1.827e-01 0.270 0.78748
## postchange 2.934e+02 1.005e+02 2.920 0.00355 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 848233.3)
##
## Null deviance: 1179308771 on 1344 degrees of freedom
## Residual deviance: 1137480856 on 1341 degrees of freedom
## AIC: 22183
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0207e+03 5.3691e+01 19.0115 < 2.2e-16 ***
## lin1 1.2188e-01 1.3561e-01 0.8987 0.368803
## lin2 4.9272e-02 1.6865e-01 0.2922 0.770169
## postchange 2.9335e+02 8.9570e+01 3.2751 0.001056 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1153.97 -721.68 89.42 629.24 1702.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 183.367 110.068 1.666 0.096666 .
## lin1 2.705 1.140 2.373 0.018199 *
## lin2 1.743 1.130 1.543 0.123746
## postchange -585.360 156.123 -3.749 0.000209 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 513383.7)
##
## Null deviance: 178831913 on 336 degrees of freedom
## Residual deviance: 170956761 on 333 degrees of freedom
## AIC: 5393.5
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 183.3665 114.6236 1.5997 0.1096592
## lin1 2.7052 1.0836 2.4965 0.0125438 *
## lin2 1.7434 1.1487 1.5176 0.1291038
## postchange -585.3603 158.3803 -3.6959 0.0002191 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "27 Regression for xbxcall model2 on 2016-11-17/blue/content change"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -981.16 -719.94 35.93 586.69 1732.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 910.83446 53.72746 16.953 <2e-16 ***
## lin1 -0.04405 0.14400 -0.306 0.760
## lin2 0.13445 0.13573 0.991 0.322
## postchange 25.18241 75.32866 0.334 0.738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 467997.3)
##
## Null deviance: 617431534 on 1319 degrees of freedom
## Residual deviance: 615884394 on 1316 degrees of freedom
## AIC: 20986
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 910.834464 51.474188 17.6950 <2e-16 ***
## lin1 -0.044052 0.141241 -0.3119 0.7551
## lin2 0.134447 0.135551 0.9919 0.3213
## postchange 25.182411 73.366660 0.3432 0.7314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1015.61 -687.32 71.64 596.81 1209.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.960 105.781 0.567 0.5712
## lin1 1.042 1.270 0.820 0.4127
## lin2 1.790 1.009 1.773 0.0772 .
## postchange -234.585 144.833 -1.620 0.1063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 409834.9)
##
## Null deviance: 128616976 on 313 degrees of freedom
## Residual deviance: 127048809 on 310 degrees of freedom
## AIC: 4955
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 59.9602 106.7762 0.5616 0.57442
## lin1 1.0419 1.2366 0.8426 0.39948
## lin2 1.7895 1.0069 1.7773 0.07552 .
## postchange -234.5853 142.0983 -1.6509 0.09877 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "28 Regression for rate_xbxcall model2 on 2016-10-11/purple/0-10%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.00907 -0.00639 -0.00231 0.00453 0.39687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.458e-03 1.238e-03 6.833 1.26e-11 ***
## lin1 8.878e-07 3.242e-06 0.274 0.784
## lin2 -8.033e-07 3.177e-06 -0.253 0.800
## postchange 1.021e-03 1.747e-03 0.585 0.559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0002541588)
##
## Null deviance: 0.33818 on 1332 degrees of freedom
## Residual deviance: 0.33778 on 1329 degrees of freedom
## AIC: -7245.1
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.4584e-03 5.1585e-04 16.3970 <2e-16 ***
## lin1 8.8783e-07 1.3373e-06 0.6639 0.5068
## lin2 -8.0335e-07 2.0697e-06 -0.3881 0.6979
## postchange 1.0213e-03 1.5056e-03 0.6783 0.4976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.009667 -0.005786 -0.001411 0.005303 0.017423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.919e-03 1.038e-03 1.849 0.065404 .
## lin1 2.328e-05 1.143e-05 2.037 0.042503 *
## lin2 2.481e-05 1.033e-05 2.401 0.016934 *
## postchange -5.397e-03 1.450e-03 -3.722 0.000233 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.294894e-05)
##
## Null deviance: 0.014472 on 326 degrees of freedom
## Residual deviance: 0.013873 on 323 degrees of freedom
## AIC: -2354.2
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9187e-03 1.0997e-03 1.7448 0.08102 .
## lin1 2.3281e-05 1.1155e-05 2.0870 0.03688 *
## lin2 2.4806e-05 9.6420e-06 2.5727 0.01009 *
## postchange -5.3972e-03 1.3545e-03 -3.9847 6.757e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "29 Regression for rate_xbxcall model2 on 2016-10-18/purple/10-30%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.01101 -0.00628 -0.00215 0.00455 0.39526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.240e-03 1.243e-03 6.631 4.87e-11 ***
## lin1 3.398e-07 3.255e-06 0.104 0.9169
## lin2 -7.543e-06 3.361e-06 -2.244 0.0250 *
## postchange 3.344e-03 1.769e-03 1.890 0.0589 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0002561539)
##
## Null deviance: 0.33616 on 1309 degrees of freedom
## Residual deviance: 0.33454 on 1306 degrees of freedom
## AIC: -7109.7
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.2400e-03 5.2875e-04 15.5839 <2e-16 ***
## lin1 3.3977e-07 1.4420e-06 0.2356 0.8137
## lin2 -7.5425e-06 5.5748e-06 -1.3530 0.1761
## postchange 3.3440e-03 2.6737e-03 1.2507 0.2110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.01552 -0.00769 -0.00309 0.00297 0.39254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.591e-03 4.553e-03 0.349 0.727
## lin1 2.184e-05 4.715e-05 0.463 0.644
## lin2 4.740e-05 4.758e-05 0.996 0.320
## postchange -3.279e-03 6.478e-03 -0.506 0.613
##
## (Dispersion parameter for gaussian family taken to be 0.0008785053)
##
## Null deviance: 0.29238 on 334 degrees of freedom
## Residual deviance: 0.29079 on 331 degrees of freedom
## AIC: -1400.8
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.5909e-03 1.0769e-03 1.4773 0.139587
## lin1 2.1842e-05 9.6498e-06 2.2635 0.023607 *
## lin2 4.7399e-05 1.7998e-05 2.6336 0.008448 **
## postchange -3.2793e-03 2.6919e-03 -1.2182 0.223147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "30 Regression for rate_xbxcall model2 on 2016-11-01/purple/30-50%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.00952 -0.00636 -0.00209 0.00449 0.39682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.729e-03 1.251e-03 7.777 1.5e-14 ***
## lin1 1.706e-06 3.287e-06 0.519 0.604
## lin2 4.397e-06 3.363e-06 1.307 0.191
## postchange -2.577e-03 1.778e-03 -1.449 0.147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0002588406)
##
## Null deviance: 0.33867 on 1309 degrees of freedom
## Residual deviance: 0.33805 on 1306 degrees of freedom
## AIC: -7096.1
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.7293e-03 1.5228e-03 6.3893 1.667e-10 ***
## lin1 1.7064e-06 2.3358e-06 0.7305 0.465065
## lin2 4.3965e-06 1.5108e-06 2.9102 0.003612 **
## postchange -2.5774e-03 1.6113e-03 -1.5995 0.109707
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.010363 -0.005432 -0.001211 0.004841 0.015513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.563e-03 9.430e-04 -2.718 0.00691 **
## lin1 3.899e-07 9.766e-06 0.040 0.96818
## lin2 2.518e-05 9.680e-06 2.602 0.00969 **
## postchange 1.591e-03 1.338e-03 1.190 0.23505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 3.768637e-05)
##
## Null deviance: 0.013999 on 336 degrees of freedom
## Residual deviance: 0.012550 on 333 degrees of freedom
## AIC: -2470.4
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.5631e-03 8.0802e-04 -3.1721 0.001513 **
## lin1 3.8993e-07 8.2985e-06 0.0470 0.962523
## lin2 2.5183e-05 1.0129e-05 2.4863 0.012908 *
## postchange 1.5913e-03 1.1830e-03 1.3451 0.178583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "31 Regression for rate_xbxcall model2 on 2016-12-15/purple/50-90%"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.012949 -0.007363 -0.001841 0.005994 0.065171
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.092e-03 7.195e-04 11.247 < 2e-16 ***
## lin1 -1.513e-06 1.857e-06 -0.815 0.415
## lin2 -1.522e-06 1.852e-06 -0.822 0.411
## postchange 5.338e-03 1.018e-03 5.242 1.85e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 8.717115e-05)
##
## Null deviance: 0.12328 on 1344 degrees of freedom
## Residual deviance: 0.11690 on 1341 degrees of freedom
## AIC: -8749.6
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.0922e-03 4.8272e-04 16.7636 < 2.2e-16 ***
## lin1 -1.5127e-06 1.3142e-06 -1.1511 0.2497
## lin2 -1.5224e-06 1.7388e-06 -0.8755 0.3813
## postchange 5.3377e-03 8.9783e-04 5.9451 2.763e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.010145 -0.006077 -0.001046 0.006201 0.017045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.882e-04 1.048e-03 0.847 0.397371
## lin1 1.385e-05 1.085e-05 1.276 0.202792
## lin2 1.336e-05 1.076e-05 1.242 0.215278
## postchange -5.574e-03 1.487e-03 -3.749 0.000209 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.655516e-05)
##
## Null deviance: 0.016558 on 336 degrees of freedom
## Residual deviance: 0.015503 on 333 degrees of freedom
## AIC: -2399.2
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.8822e-04 9.2815e-04 0.9570 0.3386
## lin1 1.3852e-05 9.0405e-06 1.5322 0.1255
## lin2 1.3357e-05 1.0755e-05 1.2420 0.2142
## postchange -5.5735e-03 1.4116e-03 -3.9482 7.873e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "32 Regression for rate_xbxcall model2 on 2016-11-17/blue/content change"
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.01198 -0.00639 -0.00184 0.00462 0.39406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.689e-03 1.254e-03 4.536 6.24e-06 ***
## lin1 -1.062e-05 3.361e-06 -3.160 0.00161 **
## lin2 -1.382e-06 3.168e-06 -0.436 0.66281
## postchange 3.391e-03 1.758e-03 1.928 0.05402 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0002549532)
##
## Null deviance: 0.33820 on 1319 degrees of freedom
## Residual deviance: 0.33552 on 1316 degrees of freedom
## AIC: -7170.3
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.6887e-03 1.3813e-03 4.1184 3.815e-05 ***
## lin1 -1.0622e-05 6.6593e-06 -1.5951 0.11070
## lin2 -1.3817e-06 1.3151e-06 -1.0507 0.29342
## postchange 3.3905e-03 1.4852e-03 2.2829 0.02243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## glm(formula = form, data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.008306 -0.005659 -0.001179 0.005061 0.031560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.782e-03 1.055e-03 1.689 0.0923 .
## lin1 8.608e-06 1.267e-05 0.679 0.4974
## lin2 9.171e-06 1.007e-05 0.911 0.3632
## postchange -3.279e-03 1.445e-03 -2.270 0.0239 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 4.078777e-05)
##
## Null deviance: 0.012973 on 313 degrees of freedom
## Residual deviance: 0.012644 on 310 degrees of freedom
## AIC: -2276.6
##
## Number of Fisher Scoring iterations: 2
##
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7822e-03 9.1340e-04 1.9512 0.05103 .
## lin1 8.6079e-06 1.0756e-05 0.8003 0.42356
## lin2 9.1706e-06 9.7077e-06 0.9447 0.34483
## postchange -3.2793e-03 1.3732e-03 -2.3881 0.01694 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Results
Step 1 Results
| idx | title | model | formula | chgDate | chgVal | aic | deviance |
|---|---|---|---|---|---|---|---|
| 1 | SMC - Hourly Chats | model2 | winchib ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | 16460.222 | 1.614176e+07 |
| 2 | SMC - Hourly Chats | model2 | winchib ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | 16682.477 | 1.904215e+07 |
| 3 | SMC - Hourly Chats | model2 | winchib ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | 16735.702 | 1.981081e+07 |
| 4 | SMC - Hourly Chat Rate per Session | model2 | rate_winchib ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | -15450.044 | 8.022000e-04 |
| 5 | SMC - Hourly Chat Rate per Session | model2 | rate_winchib ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | -15301.081 | 8.961000e-04 |
| 6 | SMC - Hourly Chat Rate per Session | model2 | rate_winchib ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | -15236.116 | 9.405000e-04 |
| 7 | SMC - Hourly Chats | model2 | wincall ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | 18983.065 | 1.053333e+08 |
| 8 | SMC - Hourly Chats | model2 | wincall ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | 18795.588 | 9.162844e+07 |
| 9 | SMC - Hourly Chats | model2 | wincall ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | 18869.232 | 9.678534e+07 |
| 10 | SMC - Hourly Chat Rate per Session | model2 | rate_wincall ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | -13029.652 | 4.850700e-03 |
| 11 | SMC - Hourly Chat Rate per Session | model2 | rate_wincall ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | -13405.499 | 3.668100e-03 |
| 12 | SMC - Hourly Chat Rate per Session | model2 | rate_wincall ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | -13364.476 | 3.781700e-03 |
| 13 | Xbox - Hourly Chats | model2 | xbxchib ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | 16763.621 | 2.242458e+07 |
| 14 | Xbox - Hourly Chats | model2 | xbxchib ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | 16344.307 | 1.995205e+07 |
| 15 | Xbox - Hourly Chats | model2 | xbxchib ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | 16209.956 | 1.800724e+07 |
| 16 | Xbox - Hourly Chats | model2 | xbxchib ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | 17322.618 | 3.064889e+07 |
| 17 | Xbox - Hourly Chats | model2 | xbxchib ~ lin1 + lin2 + postchange | 2016-11-17 | content change | 16342.355 | 1.826516e+07 |
| 18 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxchib ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | -12363.562 | 7.261400e-03 |
| 19 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxchib ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | -12178.442 | 6.983200e-03 |
| 20 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxchib ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | -12165.739 | 7.051200e-03 |
| 21 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxchib ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | -13171.038 | 4.366700e-03 |
| 22 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxchib ~ lin1 + lin2 + postchange | 2016-11-17 | content change | -12265.578 | 7.068100e-03 |
| 23 | Xbox - Hourly Chats | model2 | xbxcall ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | 21376.884 | 7.140624e+08 |
| 24 | Xbox - Hourly Chats | model2 | xbxcall ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | 20957.716 | 6.752058e+08 |
| 25 | Xbox - Hourly Chats | model2 | xbxcall ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | 20848.013 | 6.209652e+08 |
| 26 | Xbox - Hourly Chats | model2 | xbxcall ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | 22183.414 | 1.137481e+09 |
| 27 | Xbox - Hourly Chats | model2 | xbxcall ~ lin1 + lin2 + postchange | 2016-11-17 | content change | 20986.199 | 6.158844e+08 |
| 28 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxcall ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | -7245.092 | 3.377771e-01 |
| 29 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxcall ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | -7109.736 | 3.345370e-01 |
| 30 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxcall ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | -7096.068 | 3.380459e-01 |
| 31 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxcall ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | -8749.633 | 1.168965e-01 |
| 32 | Xbox - Hourly Chat Rate per Session | model2 | rate_xbxcall ~ lin1 + lin2 + postchange | 2016-11-17 | content change | -7170.257 | 3.355184e-01 |
Step 2 Results
| idx | title | model | formula | chgDate | chgVal | chgCoef.fit | chgCoef.smry | chgStd.smry | chgTval.smry | chgPval.smry | s |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | SMC - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | 9.6937243 | 9.6937243 | 25.4228096 | 0.3813003 | 0.7032236 | | |
| 2 | SMC - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | -19.8954312 | -19.8954312 | 24.3552471 | -0.8168848 | 0.4145783 | | |
| 3 | SMC - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | 30.8848242 | 30.8848242 | 24.7849350 | 1.2461128 | 0.2135989 | | |
| 4 | SMC - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | 0.0001103 | 0.0001103 | 0.0001694 | 0.6509015 | 0.5155589 | | |
| 5 | SMC - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | 0.0000412 | 0.0000412 | 0.0001746 | 0.2357684 | 0.8137573 | | |
| 6 | SMC - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | 0.0000515 | 0.0000515 | 0.0001785 | 0.2883932 | 0.7732252 | | |
| 7 | SMC - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | -63.1475081 | -63.1475081 | 56.5905347 | -1.1158670 | 0.2652837 | | |
| 8 | SMC - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | -30.3619277 | -30.3619277 | 51.4377453 | -0.5902655 | 0.5554130 | | |
| 9 | SMC - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | -2.3885621 | -2.3885621 | 50.0627327 | -0.0477114 | 0.9619749 | | |
| 10 | SMC - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-08-17 | 0_30 | -0.0004794 | -0.0004794 | 0.0003526 | -1.3594917 | 0.1749107 | | |
| 11 | SMC - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-1 | 30_50 | -0.0001361 | -0.0001361 | 0.0003679 | -0.3700223 | 0.7116011 | | |
| 12 | SMC - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-09-07 | 50_100 | -0.0003447 | -0.0003447 | 0.0003585 | -0.9615609 | 0.3369679 | | |
| 13 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | -22.0893426 | -22.0893426 | 25.1464294 | -0.8784286 | 0.3803641 | | |
| 14 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | -44.0174327 | -44.0174327 | 24.0911387 | -1.8271213 | 0.0685819 | | . |
| 15 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | -6.2570898 | -6.2570898 | 24.6679937 | -0.2536522 | 0.7999208 | | |
| 16 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | -98.4642598 | -98.4642598 | 24.5202226 | -4.0156348 | 0.0000733 | | *** |
| 17 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-17 | content change | -43.4399858 | -43.4399858 | 26.9109499 | -1.6142123 | 0.1074988 | | |
| 18 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | -0.0009049 | -0.0009049 | 0.0002812 | -3.2182581 | 0.0014206 | | ** |
| 19 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | -0.0007128 | -0.0007128 | 0.0008525 | -0.8361953 | 0.4036483 | | |
| 20 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | -0.0000854 | -0.0000854 | 0.0002905 | -0.2940537 | 0.7689001 | | |
| 21 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | -0.0009994 | -0.0009994 | 0.0002865 | -3.4875733 | 0.0005528 | | *** |
| 22 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-17 | content change | -0.0004212 | -0.0004212 | 0.0003152 | -1.3361586 | 0.1824774 | | |
| 23 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | -190.5308297 | -190.5308297 | 157.7400158 | -1.2078789 | 0.2279775 | | |
| 24 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | -211.0343513 | -211.0343513 | 155.7808819 | -1.3546871 | 0.1764412 | | |
| 25 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | 74.6512694 | 74.6512694 | 143.5981888 | 0.5198622 | 0.6035050 | | |
| 26 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | -585.3603018 | -585.3603018 | 156.1233916 | -3.7493440 | 0.0002090 | | *** |
| 27 | Xbox - Hourly Chats | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-17 | content change | -234.5853205 | -234.5853205 | 144.8325824 | -1.6196999 | 0.1063136 | | |
| 28 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-11 | 0_10 | -0.0053972 | -0.0053972 | 0.0014501 | -3.7220550 | 0.0002330 | | *** |
| 29 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-10-18 | 10_30 | -0.0032793 | -0.0032793 | 0.0064779 | -0.5062257 | 0.6130352 | | |
| 30 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-01 | 30_50 | 0.0015913 | 0.0015913 | 0.0013376 | 1.1895974 | 0.2350522 | | |
| 31 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-12-15 | 50_90 | -0.0055735 | -0.0055735 | 0.0014867 | -3.7488508 | 0.0002094 | | *** |
| 32 | Xbox - Hourly Chat Rate per Session | model2 | step1residuals ~ lin1 + lin2 + postchange | 2016-11-17 | content change | -0.0032793 | -0.0032793 | 0.0014449 | -2.2696579 | 0.0239149 | | * |
Done
## [1] "Version 0.1 created on 24 Mar 2017 - 16:55:10 took 85.8 secs"